Estimation of Economic Indicators using Residual Neural Network ResNet50

The article is concerned with the following issues: definitions, indicators of trust were reviewed; the working hypotheses of the research were formed; the choice of factors related to the trust indices was made; cluster analysis of the relationship between individual trust indices and economic indicators was carried out; a correlation analysis of the relationship between individual trust indices and socio-cultural indicators was conducted; a neural network for modeling the general index of trust based on a well-founded set of economic and socio-cultural indicators was developed. The hypothesis about the influence of socio-cultural factors on trust and out of which there was distinguished a relation to a specific religion. By means of correlation analysis and neural networks, it was shown that Protestantism and Catholicism are the most significant religions that affect the general index of interpersonal trust. However, atheism has a more significant impact. Following 198 observations, each of which represented the country for a given year in the period from 1995 to 2014, the neural network produced satisfactory results in forecasting the total trust index on the basis of the following factors: GDP per capita, GINI coefficient, atheism (percentage of population, support such an attitude to religion). The neural network recognized 89.9% of the data and 90% of the test set indicating that the network got adjusted and could be used for modeling. The scatter diagram for a 5% error indicates that most of the data is within the required value. But it should be noted, that the model overestimates trust in Ukraine at the end of the analyzed period. This gives grounds for the assumption that in Ukraine there are additional factors that negatively affect interpersonal trust.

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Prediction of Levelized Cost of Electricity for Solar Photovoltaic Systems by Using Neural Networks

Vestnik MEI ◽

10.24160/1993-6982-2021-4-53-58 ◽

2021 ◽

pp. 53-58

Author(s):

Natalya S. Filippchenkova ◽

Keyword(s):

Neural Network ◽

Carbon Dioxide Emissions ◽

Economic Indicators ◽

Photovoltaic Systems ◽

Special Importance ◽

Solar Photovoltaic ◽

Levelized Cost Of Electricity ◽

Levelized Cost ◽

Marquardt Algorithm ◽

Nonlinear Autoregressive

Elaboration of a new approach to the development of models for predicting the economic indicators of solar photovoltaic systems by using artificial neural network algorithms is becoming of special importance. As is known, the relationships between economic indicators are often difficult to identify. Nonlinear autoregressive models can provide more reliable results than those obtained from predictive linear models based on vector autoregression. The article presents the results from the development of a mathematical model for predicting the levelized cost of energy (LCOE) for solar photovoltaic systems based on a nonlinear autoregressive neural network with exogenous inputs (NARX). A two-layer NARX network with hidden sigmoid neurons and linear output neurons has been developed. The input layer is made up of the following variables: the amount of power consumed from solar photovoltaic systems around the world; the total worldwide energy consumption; domestic consumption of energy, gas, coal, and lignite; the shares of renewable energy, wind and solar energy in electricity generation; carbon dioxide emissions from fuel combustion; the price of Brent oil in US dollars, and the average price for natural gas. The output layer determines the LCOE values for solar photovoltaic systems. The developed NARX network was trained on the basis of retrospective data for 2005-2010 using the Levenberg-Marquardt algorithm. The correlation coefficient value achieved in the course of training made 0.99904, and the mean square error value was in the range from 0.00042 to 0.0029

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Nexus between Energy Usability, Economic Indicators and Environmental Sustainability in Four ASEAN Countries: A Non-Linear Autoregressive Exogenous Neural Network Modelling Approach

Processes ◽

10.3390/pr8121529 ◽

2020 ◽

Vol 8 (12) ◽

pp. 1529

Author(s):

Siti Indati Mustapa ◽

Freida Ozavize Ayodele ◽

Bamidele Victor Ayodele ◽

Norsyahida Mohammad

Keyword(s):

Neural Network ◽

Co2 Emissions ◽

Environmental Sustainability ◽

The Philippines ◽

Economic Indicators ◽

Coal Consumption ◽

Non Linear ◽

Asean Countries ◽

Mean Square Errors ◽

Narx Model

This study investigates the use of a non-linear autoregressive exogenous neural network (NARX) model to investigate the nexus between energy usability, economic indicators, and carbon dioxide (CO2) emissions in four Association of South East Asian Nations (ASEAN), namely Malaysia, Thailand, Indonesia, and the Philippines. Optimized NARX model architectures of 5-29-1, 5-19-1, 5-17-1, 5-13-1 representing the input nodes, hidden neurons and the output units were obtained from the series of models configured. Based on the relationship between the input variables, CO2 emissions were predicted with a high correlation coefficient (R) > 0.9. and low mean square errors (MSE) of 3.92 × 10−21, 4.15 × 10−23, 2.02 × 10−19, 1.32 × 10−20 for Malaysia, Thailand, Indonesia, and the Philippines, respectively. Coal consumption has the highest level of influence on CO2 emissions in the four ASEAN countries based on the sensitivity analysis. These findings suggest that government policies in the four ASEAN countries should be more intensified on strategies to reduce CO2 emissions in relationship with the energy and economic indicators.

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USE OF ARTIFICIAL NEURAL NETWORKS TO PREDICT TERRITORIAL ECONOMIC INDICATORS

Globalization and Business ◽

10.35945/gb.2019.08.019 ◽

2019 ◽

Vol 4 (8) ◽

pp. 143-146

Author(s):

Gocha Ugulava

Keyword(s):

Neural Network ◽

Neural Networks ◽

Artificial Neural Network ◽

Artificial Neural Networks ◽

Linear Regression ◽

Economic Indicators ◽

Regional Economic ◽

The Neural Network ◽

Artificial Neural ◽

Predicted Values

Modern economic science is unthinkable without predicting and planning the prospects for economic life development. There are many different mathematical and statistical tools in the arsenal of scientists as well as practitioners and economists today in purpose of forecasting. To date, one of the most prominent effective tools for data analytics is artificial neural networks. Artificial Neural Network - is a mathematical mod- el created in the likeness of a human neural network, and its software and hardware implementation. We carried out modeling and forecasting of regional economic indicators using the artificial neural network of the three-layer perceptron architecture. The network architecture and neuron settings were automatically formatted through the programming language R and its package - Neuralnet. During the forecasting phase, the data vectors were presented as data frame in five input parameters (DFI, FAI, EMP, BT, CPI), according to the neural network forecast of the regional gross domestic product (RGDP_NN) was calculated. All data are from the Imereti region and are taken from official GeoStat sources. Forecasting was done at the same time scale (2006-2017) to enable us to compare the predicted values with the actual ones to verify the level of fore- cast accuracy. We also tested the results of the neural network in another way - compared to the predicted values using multiple linear regression on the same data. The accuracy of the predicted values calculated by the neural network was quite high, which was not declining but slightly ahead of the accuracy coefficients of the predicted values obtained through linear regression. Also, the predictive values calculated by the neural network with high adequacy and accuracy were compared with actual, existing ones. Presented material shows that the use of artificial neural networks for the prediction of territorial economic indicators is reasonable and justified. Their role in analyzing and predicting indicators that are characterized by nonstationarity, dynamism, lack of a definite trend, periodicity, nonlinear structure is especially increased. It is therefore advisable to apply this method in regional economic studies, in predicting territorial development plans, strategies, targets and indicators.

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